{"id":240597,"links":{},"created":"2025-01-19T01:44:52.884318+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00240597","sets":["1164:4061:11479:11787"]},"path":["11787"],"owner":"44499","recid":"240597","title":["Preliminary Investigation of End-to-end Indoor Trajectory Prediction Based on 2D Floorplan Image and Graph Neural Network"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-11-11"},"_buckets":{"deposit":"134b01fd-7834-44b9-9876-8311770c4794"},"_deposit":{"id":"240597","pid":{"type":"depid","value":"240597","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Preliminary Investigation of End-to-end Indoor Trajectory Prediction Based on 2D Floorplan Image and Graph Neural Network","author_link":["660395","660394","660392","660393"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Preliminary Investigation of End-to-end Indoor Trajectory Prediction Based on 2D Floorplan Image and Graph Neural Network"},{"subitem_title":"Preliminary Investigation of End-to-end Indoor Trajectory Prediction Based on 2D Floorplan Image and Graph Neural Network","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ウェアラブル","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2024-11-11","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/240597/files/IPSJ-UBI24084022.pdf","label":"IPSJ-UBI24084022.pdf"},"date":[{"dateType":"Available","dateValue":"2026-11-11"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-UBI24084022.pdf","filesize":[{"value":"1.5 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"36"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"3aba315b-c2bc-4db4-b417-f2feeae99c69","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yuqiao, Wang"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takuya, Maekawa"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yuqiao, Wang","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takuya, Maekawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11838947","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8698","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"Predicting movement trajectories of individuals within indoor environments is essential for applications such as improving navigation systems and enhancing spatial awareness in smart buildings. Traditional trajectory prediction methods often perform map-matching by leveraging floormap information, resulting in complex and costly systems due to feature extraction from the floormap information and integrating the extracted floormap information into computationally expensive non-linear systems such as particle filters. This study addresses these challenges by introducing a user-friendly, end-to-end model that eliminates the need for complicated pre-processing or specialized data collection. Our approach simplifies the task by using only Inertial Measurement Units (IMU) data and a 2D floorplan image to construct a neural network-based trajectory prediction system. By leveraging Graph Neural Networks (GNNs) to integrate user's positional information into the floor map information and Long Short-Term Memory (LSTM) networks to capture the temporal dynamics of user movements, the model reconstructs the trajectory of the pedestrian based on IMU data. This method offers a practical and accessible solution for accurate trajectory prediction in complex indoor settings by integrating spatial and temporal data into a unified framework, demonstrating the effectiveness of GNNs in processing spatial structures for real-world applications.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Predicting movement trajectories of individuals within indoor environments is essential for applications such as improving navigation systems and enhancing spatial awareness in smart buildings. Traditional trajectory prediction methods often perform map-matching by leveraging floormap information, resulting in complex and costly systems due to feature extraction from the floormap information and integrating the extracted floormap information into computationally expensive non-linear systems such as particle filters. This study addresses these challenges by introducing a user-friendly, end-to-end model that eliminates the need for complicated pre-processing or specialized data collection. Our approach simplifies the task by using only Inertial Measurement Units (IMU) data and a 2D floorplan image to construct a neural network-based trajectory prediction system. By leveraging Graph Neural Networks (GNNs) to integrate user's positional information into the floor map information and Long Short-Term Memory (LSTM) networks to capture the temporal dynamics of user movements, the model reconstructs the trajectory of the pedestrian based on IMU data. This method offers a practical and accessible solution for accurate trajectory prediction in complex indoor settings by integrating spatial and temporal data into a unified framework, demonstrating the effectiveness of GNNs in processing spatial structures for real-world applications.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告ユビキタスコンピューティングシステム（UBI）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-11-11","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"22","bibliographicVolumeNumber":"2024-UBI-84"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T07:55:40.760013+00:00"}